MEKA: A Multi-label Extension to WEKA

The MEKA project provides an open source implementation of methods for multi-label learning and evaluation. In multi-label classification, we want to predict multiple output variables for each input instance. This different from the 'standard' case (binary, or multi-class classification) which involves only a single target variable.
MEKA is based on the WEKA Machine Learning Toolkit; it includes dozens of multi-label methods from the scientific literature, as well as a wrapper to the related MULAN framework.

A collection of multi-label and multi-target datasets is available here. Even more datasets are available at the MULAN Website (note that MULAN indexes labels as the final attributes, whereas MEKA indexs as the beginning). See the MEKA Tutorial for more information.